A startup is like a rat in a maze searching for a piece of cheese. The cheese in the startup's case is product-market fit, that pivotal point when the startup can scale and monetize the business.

In the maze, the startup has a dazzling amount of choices of where to go. Should we build this new feature? Should we try this new idea we have for a product? Should we backtrack and completely change our idea?

Lean startups use a strategy called "Minimum Viable Products" to help navigate the maze. The idea is that a startup formulates hypotheses about what users want or do not want; each of these hypotheses is a "turn" in the maze. A "Minimum Viable Product" is the smallest test that will let the startup know whether their "turn" was a good one. A startup wants to stop going the wrong direction as early as possible.

A "Minimum Viable Product" can be anything from a working application to an SEO'd survey that will gauge interest in an idea. "Minimum Viable Products" have beenwrittenaboutextensively.

The term "Minimum Viable Product" is a misnomer

However, the term "Minimum Viable Product" is a misnomer. The real goal is to test hypotheses as fast as possible, and being minimal is just a side effect of being fast. "Fastest Viable Product" is a more appropriate name.

When I finished college, I was incredibly naive when it came to finding a great job. I knew that I wanted to work at a small startup but didn't know how to find that great opportunity. I didn't know what questions to ask to evaluate a company, and I didn't know how I should present myself during the recruitment process.

Now I'm a few years out of college and I have that kick-ass job I was looking for. My dual experiences of looking for a job and being on the other side recruiting programmers have taught me quite a bit about what it takes to get a great job at a kick-ass startup.

Here are my tips, from preparing for the job search process to finding great startups to applying and getting the job. If you have any tips of your own, be sure to leave them in the comments!

I'm a longtime Java, Ruby, and Python programmer. Yet Clojure is the first language I've used that I truly enjoy using on a daily basis.

Clojure is a special language. There have been many attempts to articulate the benefits of Lisp-based languages before, but most of these attempts seem to end in futility. Until you use the language, it's hard to understand why functional programming, macros, and immutability are such a big deal.

So I'm going to take a different approach in explaining the virtues of Clojure. I'm going to start off somewhat unusually by talking about SQL, show how querying is done fundamentally differently in Clojure, and transition from there into a broader discussion about domain specific languages, accidental complexity, and how Clojure solves problems that have plagued programmers throughout programming's history.

The problem with SQL

SQL is a language for querying relational databases. SQL is one of the most successful technologies ever, but very few technologies can claim to have led to as much unnecessary complexity as SQL.

SQL solves the problem of querying a relational database in a concise, expressive manner. In that regard, SQL does a very good job.

The problem with SQL is that it's a custom language. Using SQL from other languages causes a host of other problems - problems that are orthogonal to querying a database. These problems are examples of accidental complexity, complexity in an application caused by the tool used to solve a problem rather than the problem itself.

The prime example of accidental complexity caused by the nature of SQL being a custom language are SQL injection attacks.

SQL injection has nothing to do with querying databases

SQL injection results from using one language from within another by doing string manipulation. This has nothing to do with querying databases, it's an integration problem. As we'll see later, it's a problem we can avoid in Clojure.

Most people flee uncertainty. Yet being able to think well under uncertainty can be very rewarding, and I've come to realize it's a skill that can be learned. Here are some tips for thinking under uncertainty.

Spencer Fry wrote a great post on "Why entrepreneurs should write." I would further add that the benefits of writing are so extraordinary that you should write a blog even if you have no readers (and regardless of whether you're an entrepreneur).

I have over 50 unfinished drafts. Some of them are just a few ideas scribbled down arguing with myself. Most of them will never be published, yet I got value out of writing all of them.

Writing makes you a better reader

Blogging has changed how I read other people's writing.

In struggling to find the right ways to structure and present my posts, I am much more attuned to what makes a good argument and what makes a bad argument. I am better at seeing holes in other people's reasoning.

At the same time, when reading I am less likely to fall into the trap of discrediting a post with weak counterclaims. In most any post, there are likely to be counterclaims that are based on exceptional cases. Internet commenters love to point these out. However, these exceptional cases miss the main thrust of the post, and by understanding the implicit backdrop behind a post's argument, I get a lot more value out of reading.

I'm also more aware of the style of good writers. I mentally take note of the ways good writers phrase their ideas. I'd always enjoyed Paul Graham's writing, but now I really appreciate how he organizes his posts. He has an awesome ability to suck you into his world and show you what it looks like from his perspective. I've learned a lot about good writing from reading Bradford Cross's blog; his posts have a clear arc and make excellent use of short paragraphs to keep the posts flowing.

Writing makes you smarter

Writing reveals holes in your thinking. When your ideas are written and looking back at you, they're a lot less convincing than when they're just in your head. Writing forces you to mature your ideas by thinking through counterarguments.

Writing helps you organize your thoughts in a coherent way. This makes you a much better conversationalist when these topics come up. I can't count the number of times I've had deeper conversations with people because I had matured my ideas offline.

Consider anything else a side benefit

Everything else writing gives you -- personal branding, networking, inbound opportunities -- are just side benefits. They're potentially very large side benefits, but they are not the main reason you should write.

I'm the first employee of BackType, a Summer '08 YC company. My joining the company increased the company size by 50%. The experience has been awesome, but I will say up front that being the first employee of a startup is not for everyone.

The best part of being the first employee of a startup is the total exposure to all parts of the company. I've learned a ton about product development, customer development, recruiting, and entrepreneurship. Additionally, I've met and connected with lots of other awesome people through the YC network. I've gotten all these benefits at relatively low risk for myself, as I still have a salary and a solid chunk of equity.

No Rules

There are a lot of rules working at most companies. You don't even realize that some of the rules are rules until you work at a company with no rules. I'm talking about the most basic things like what hours you work, what days of the week you work, what tools you use, and whether you come into the office or not.

Because there are no rules, you have to be able to set your own direction and make decisions on your own.

I would posit a guess that this company uses Java for the majority of its work and uses MySQL on the back-end. Naturally, the company wants to recruit people who share that skill set and can "jump right in" and contribute.

This mindset is fundamentally flawed. A company should be hiring for problem solving skills, programming ability, and cultural fit, not for any specific skill set.

If anything, a company should prefer candidates who are experienced in a different set of technologies. Any worthwhile programmer will be able to pick up the technologies necessary to work with the existing code base. Hiring people with different skill sets gives the team instant experience in a new set of tools for solving problems.

Having a permissive open source policy is important if a company wants to recruit truly stellar programmers. Or put another way: great programmers will be less inclined to work for you if you have a restrictive open source policy because being involved in open source projects is one of the best ways for a programmer to increase his market value.

Traditional methods for measuring programming ability are ineffective

The job market for programmers, especially the top programmers, is notoriously inefficient. This inefficiency is due to employers lacking good methods for evaluating programmers. The standard techniques used to evaluate programmers -- resumes, on-the-spot coding questions, take-home projects -- are at best crude approximations of a programmer's ability, and none of them will be indicators of the truly visionary people. Sure, there are other indicators like being involved in successful companies or having past impressive titles, but those are still indirect indicators of programming ability.

If you're a programmer, this difficulty in measuring your skill means its really difficult to make a potential employer's perceived value of you match your actual value. Top programmers aren't differentiated from the next tier of programmers and get badly mispriced in the market. Top programmers need better mechanisms to communicate their value so that they can be priced more fairly in the market.

In this tutorial for Cascalog, we are going to create part of the back-end for a simplified version of a Facebook-like news feed. In doing so we are going to walk through an end-to-end example of running Cascalog on a production cluster. If you're new to Cascalog, you should first look at the introductory tutorials here and here.

The code and sample data for the example presented in this tutorial can be found on Github.